{
 "nbformat": 4,
 "nbformat_minor": 0,
 "metadata": {
  "colab": {
   "name": "Cycle GAN",
   "provenance": [],
   "collapsed_sections": [],
   "toc_visible": true
  },
  "kernelspec": {
   "name": "python3",
   "language": "python",
   "display_name": "Python 3"
  },
  "accelerator": "GPU"
 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AYV_dMVDxyc2"
   },
   "source": [
    "[![Github](https://img.shields.io/github/stars/labmlai/annotated_deep_learning_paper_implementations?style=social)](https://github.com/labmlai/annotated_deep_learning_paper_implementations)\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/gan/wasserstein/experiment.ipynb)\n",
    "\n",
    "## DCGAN\n",
    "\n",
    "This is an experiment training DCGAN model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AahG_i2y5tY9"
   },
   "source": [
    "Install the `labml-nn` package"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "ZCzmCrAIVg0L",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "2fe2685f-731c-4c47-854e-a4f00e485281"
   },
   "source": [
    "!pip install labml-nn"
   ],
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "stream",
     "text": [
      "Collecting labml-nn\n",
      "\u001B[?25l  Downloading https://files.pythonhosted.org/packages/9d/bb/a7a6f69ab6e21de2398b5f6b0b2bb47b430e4a20ae2c8710c489e02813be/labml_nn-0.4.81-py3-none-any.whl (118kB)\n",
      "\r\u001B[K     |██▊                             | 10kB 22.6MB/s eta 0:00:01\r\u001B[K     |█████▌                          | 20kB 14.6MB/s eta 0:00:01\r\u001B[K     |████████▎                       | 30kB 12.9MB/s eta 0:00:01\r\u001B[K     |███████████                     | 40kB 12.1MB/s eta 0:00:01\r\u001B[K     |█████████████▉                  | 51kB 8.2MB/s eta 0:00:01\r\u001B[K     |████████████████▋               | 61kB 8.7MB/s eta 0:00:01\r\u001B[K     |███████████████████▍            | 71kB 8.9MB/s eta 0:00:01\r\u001B[K     |██████████████████████▏         | 81kB 9.9MB/s eta 0:00:01\r\u001B[K     |█████████████████████████       | 92kB 8.9MB/s eta 0:00:01\r\u001B[K     |███████████████████████████▊    | 102kB 8.0MB/s eta 0:00:01\r\u001B[K     |██████████████████████████████▌ | 112kB 8.0MB/s eta 0:00:01\r\u001B[K     |████████████████████████████████| 122kB 8.0MB/s \n",
      "\u001B[?25hRequirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (from labml-nn) (1.7.0+cu101)\n",
      "Collecting labml-helpers>=0.4.72\n",
      "  Downloading https://files.pythonhosted.org/packages/ec/58/2b7dcfde4565134ad97cdfe96ad7070fef95c37be2cbc066b608c9ae5c1d/labml_helpers-0.4.72-py3-none-any.whl\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from labml-nn) (1.19.5)\n",
      "Collecting labml>=0.4.94\n",
      "\u001B[?25l  Downloading https://files.pythonhosted.org/packages/99/b2/3a424548d74a88ce565b38f6b7e707e7c2f00bf8c7c575a1c251807e4896/labml-0.4.94-py3-none-any.whl (99kB)\n",
      "\u001B[K     |████████████████████████████████| 102kB 8.2MB/s \n",
      "\u001B[?25hCollecting einops\n",
      "  Downloading https://files.pythonhosted.org/packages/5d/a0/9935e030634bf60ecd572c775f64ace82ceddf2f504a5fd3902438f07090/einops-0.3.0-py2.py3-none-any.whl\n",
      "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.6/dist-packages (from torch->labml-nn) (3.7.4.3)\n",
      "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch->labml-nn) (0.16.0)\n",
      "Requirement already satisfied: dataclasses in /usr/local/lib/python3.6/dist-packages (from torch->labml-nn) (0.8)\n",
      "Collecting gitpython\n",
      "\u001B[?25l  Downloading https://files.pythonhosted.org/packages/d7/cb/ec98155c501b68dcb11314c7992cd3df6dce193fd763084338a117967d53/GitPython-3.1.12-py3-none-any.whl (159kB)\n",
      "\u001B[K     |████████████████████████████████| 163kB 9.9MB/s \n",
      "\u001B[?25hRequirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from labml>=0.4.94->labml-nn) (3.13)\n",
      "Collecting gitdb<5,>=4.0.1\n",
      "\u001B[?25l  Downloading https://files.pythonhosted.org/packages/48/11/d1800bca0a3bae820b84b7d813ad1eff15a48a64caea9c823fc8c1b119e8/gitdb-4.0.5-py3-none-any.whl (63kB)\n",
      "\u001B[K     |████████████████████████████████| 71kB 8.6MB/s \n",
      "\u001B[?25hCollecting smmap<4,>=3.0.1\n",
      "  Downloading https://files.pythonhosted.org/packages/d5/1e/6130925131f639b2acde0f7f18b73e33ce082ff2d90783c436b52040af5a/smmap-3.0.5-py2.py3-none-any.whl\n",
      "Installing collected packages: smmap, gitdb, gitpython, labml, labml-helpers, einops, labml-nn\n",
      "Successfully installed einops-0.3.0 gitdb-4.0.5 gitpython-3.1.12 labml-0.4.94 labml-helpers-0.4.72 labml-nn-0.4.81 smmap-3.0.5\n"
     ],
     "name": "stdout"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SE2VUQ6L5zxI"
   },
   "source": [
    "Imports"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "0hJXx_g0wS2C"
   },
   "source": [
    "\n",
    "from labml import experiment\n",
    "from labml_nn.gan.wasserstein.experiment import Configs"
   ],
   "execution_count": 14,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lpggo0wM6qb-"
   },
   "source": [
    "Create an experiment"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "bFcr9k-l4cAg"
   },
   "source": [
    "experiment.create(name=\"mnist_wgan\")"
   ],
   "execution_count": 15,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-OnHLi626tJt"
   },
   "source": [
    "Initialize configurations"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "Piz0c5f44hRo"
   },
   "source": [
    "conf = Configs()"
   ],
   "execution_count": 16,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wwMzCqpD6vkL"
   },
   "source": [
    "Set experiment configurations and assign a configurations dictionary to override configurations"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17
    },
    "id": "e6hmQhTw4nks",
    "outputId": "4be767af-0ebd-4c35-8da0-0e532495e037"
   },
   "source": [
    "experiment.configs(conf,\n",
    "                   {\n",
    "                       'discriminator': 'cnn',\n",
    "                       'generator': 'cnn',\n",
    "                       'label_smoothing': 0.01,\n",
    "                       'generator_loss': 'wasserstein',\n",
    "                       'discriminator_loss': 'wasserstein',\n",
    "                   })"
   ],
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<pre style=\"overflow-x: scroll;\"></pre>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KJZRf8527GxL"
   },
   "source": [
    "Start the experiment and run the training loop."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 649
    },
    "id": "aIAWo7Fw5DR8",
    "outputId": "e3b02247-8ff9-47b5-8f52-49c9e3b8377f"
   },
   "source": [
    "with experiment.start():\n",
    "    conf.run()"
   ],
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<pre style=\"overflow-x: scroll;\"></pre>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 576x720 with 6 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 576x720 with 6 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "\u001B[0;32m<ipython-input-18-0592df1e05e4>\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[0;32mwith\u001B[0m \u001B[0mexperiment\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mstart\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 2\u001B[0;31m     \u001B[0mconf\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mrun\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      3\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/ml/labmlai/annotated_deep_learning_paper_implementations/labml_helpers/train_valid.py\u001B[0m in \u001B[0;36mrun\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m    246\u001B[0m         \u001B[0m_\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtrainer\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    247\u001B[0m         \u001B[0;32mfor\u001B[0m \u001B[0m_\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtraining_loop\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 248\u001B[0;31m             \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mrun_step\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    249\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    250\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0msample\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/ml/labmlai/annotated_deep_learning_paper_implementations/labml_helpers/train_valid.py\u001B[0m in \u001B[0;36mrun_step\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m    234\u001B[0m             \u001B[0;32mwith\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mmode\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mupdate\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mis_train\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mTrue\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    235\u001B[0m                 \u001B[0;32mwith\u001B[0m \u001B[0mtracker\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mnamespace\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m'train'\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 236\u001B[0;31m                     \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtrainer\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    237\u001B[0m             \u001B[0;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mvalidator\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    238\u001B[0m                 \u001B[0;32mwith\u001B[0m \u001B[0mtracker\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mnamespace\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m'valid'\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/ml/labmlai/annotated_deep_learning_paper_implementations/labml_helpers/train_valid.py\u001B[0m in \u001B[0;36m__call__\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m    136\u001B[0m                 \u001B[0msm\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mon_epoch_start\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    137\u001B[0m         \u001B[0;32mwith\u001B[0m \u001B[0mtorch\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mset_grad_enabled\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mmode\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mis_train\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 138\u001B[0;31m             \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m__iterate\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    139\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    140\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_batch_index\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcompleted\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/ml/labmlai/annotated_deep_learning_paper_implementations/labml_helpers/train_valid.py\u001B[0m in \u001B[0;36m__iterate\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m    147\u001B[0m                 \u001B[0mmonit\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mprogress\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    148\u001B[0m             \u001B[0;32mwhile\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_batch_index\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0miteration_completed\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 149\u001B[0;31m                 \u001B[0mbatch\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mnext\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m__iterable\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    150\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    151\u001B[0m                 \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mstep\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mbatch\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_batch_index\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/miniconda/envs/torch/lib/python3.8/site-packages/torch/utils/data/dataloader.py\u001B[0m in \u001B[0;36m__next__\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m    433\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_sampler_iter\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    434\u001B[0m             \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_reset\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 435\u001B[0;31m         \u001B[0mdata\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_next_data\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    436\u001B[0m         \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_num_yielded\u001B[0m \u001B[0;34m+=\u001B[0m \u001B[0;36m1\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    437\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_dataset_kind\u001B[0m \u001B[0;34m==\u001B[0m \u001B[0m_DatasetKind\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mIterable\u001B[0m \u001B[0;32mand\u001B[0m\u001B[0;31m \u001B[0m\u001B[0;31m\\\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/miniconda/envs/torch/lib/python3.8/site-packages/torch/utils/data/dataloader.py\u001B[0m in \u001B[0;36m_next_data\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m    473\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0m_next_data\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    474\u001B[0m         \u001B[0mindex\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_next_index\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m  \u001B[0;31m# may raise StopIteration\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 475\u001B[0;31m         \u001B[0mdata\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_dataset_fetcher\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfetch\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mindex\u001B[0m\u001B[0;34m)\u001B[0m  \u001B[0;31m# may raise StopIteration\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    476\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_pin_memory\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    477\u001B[0m             \u001B[0mdata\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0m_utils\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mpin_memory\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mpin_memory\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdata\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/miniconda/envs/torch/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py\u001B[0m in \u001B[0;36mfetch\u001B[0;34m(self, possibly_batched_index)\u001B[0m\n\u001B[1;32m     42\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0mfetch\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mpossibly_batched_index\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     43\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mauto_collation\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 44\u001B[0;31m             \u001B[0mdata\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m[\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdataset\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0midx\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;32mfor\u001B[0m \u001B[0midx\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mpossibly_batched_index\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     45\u001B[0m         \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     46\u001B[0m             \u001B[0mdata\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdataset\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mpossibly_batched_index\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/miniconda/envs/torch/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py\u001B[0m in \u001B[0;36m<listcomp>\u001B[0;34m(.0)\u001B[0m\n\u001B[1;32m     42\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0mfetch\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mpossibly_batched_index\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     43\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mauto_collation\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 44\u001B[0;31m             \u001B[0mdata\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m[\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdataset\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0midx\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;32mfor\u001B[0m \u001B[0midx\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mpossibly_batched_index\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     45\u001B[0m         \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     46\u001B[0m             \u001B[0mdata\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdataset\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mpossibly_batched_index\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/miniconda/envs/torch/lib/python3.8/site-packages/torchvision/datasets/mnist.py\u001B[0m in \u001B[0;36m__getitem__\u001B[0;34m(self, index)\u001B[0m\n\u001B[1;32m    104\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    105\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtransform\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 106\u001B[0;31m             \u001B[0mimg\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtransform\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mimg\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    107\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    108\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtarget_transform\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/miniconda/envs/torch/lib/python3.8/site-packages/torchvision/transforms/transforms.py\u001B[0m in \u001B[0;36m__call__\u001B[0;34m(self, img)\u001B[0m\n\u001B[1;32m     65\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0m__call__\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mimg\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     66\u001B[0m         \u001B[0;32mfor\u001B[0m \u001B[0mt\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtransforms\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 67\u001B[0;31m             \u001B[0mimg\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mt\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mimg\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     68\u001B[0m         \u001B[0;32mreturn\u001B[0m \u001B[0mimg\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     69\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/miniconda/envs/torch/lib/python3.8/site-packages/torch/nn/modules/module.py\u001B[0m in \u001B[0;36m_call_impl\u001B[0;34m(self, *input, **kwargs)\u001B[0m\n\u001B[1;32m    725\u001B[0m             \u001B[0mresult\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_slow_forward\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0minput\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    726\u001B[0m         \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 727\u001B[0;31m             \u001B[0mresult\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mforward\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0minput\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    728\u001B[0m         for hook in itertools.chain(\n\u001B[1;32m    729\u001B[0m                 \u001B[0m_global_forward_hooks\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mvalues\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/miniconda/envs/torch/lib/python3.8/site-packages/torchvision/transforms/transforms.py\u001B[0m in \u001B[0;36mforward\u001B[0;34m(self, tensor)\u001B[0m\n\u001B[1;32m    224\u001B[0m             \u001B[0mTensor\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mNormalized\u001B[0m \u001B[0mTensor\u001B[0m \u001B[0mimage\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    225\u001B[0m         \"\"\"\n\u001B[0;32m--> 226\u001B[0;31m         \u001B[0;32mreturn\u001B[0m \u001B[0mF\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mnormalize\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mtensor\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mmean\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mstd\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0minplace\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    227\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    228\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0m__repr__\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/miniconda/envs/torch/lib/python3.8/site-packages/torchvision/transforms/functional.py\u001B[0m in \u001B[0;36mnormalize\u001B[0;34m(tensor, mean, std, inplace)\u001B[0m\n\u001B[1;32m    276\u001B[0m     \u001B[0mmean\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mtorch\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mas_tensor\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmean\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdtype\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mdtype\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdevice\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mtensor\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdevice\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    277\u001B[0m     \u001B[0mstd\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mtorch\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mas_tensor\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mstd\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdtype\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mdtype\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdevice\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mtensor\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdevice\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 278\u001B[0;31m     \u001B[0;32mif\u001B[0m \u001B[0;34m(\u001B[0m\u001B[0mstd\u001B[0m \u001B[0;34m==\u001B[0m \u001B[0;36m0\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0many\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    279\u001B[0m         \u001B[0;32mraise\u001B[0m \u001B[0mValueError\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m'std evaluated to zero after conversion to {}, leading to division by zero.'\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mformat\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdtype\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    280\u001B[0m     \u001B[0;32mif\u001B[0m \u001B[0mmean\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mndim\u001B[0m \u001B[0;34m==\u001B[0m \u001B[0;36m1\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/miniconda/envs/torch/lib/python3.8/site-packages/torch/tensor.py\u001B[0m in \u001B[0;36mwrapped\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m     22\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0mwrapped\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     23\u001B[0m         \u001B[0;32mfrom\u001B[0m \u001B[0mtorch\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0moverrides\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mhas_torch_function\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mhandle_torch_function\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 24\u001B[0;31m         \u001B[0;32mif\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0mall\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mtype\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mt\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0mTensor\u001B[0m \u001B[0;32mfor\u001B[0m \u001B[0mt\u001B[0m \u001B[0;32min\u001B[0m \u001B[0margs\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;32mand\u001B[0m \u001B[0mhas_torch_function\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     25\u001B[0m             \u001B[0;32mreturn\u001B[0m \u001B[0mhandle_torch_function\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mwrapped\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     26\u001B[0m         \u001B[0;32mtry\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/miniconda/envs/torch/lib/python3.8/site-packages/torch/overrides.py\u001B[0m in \u001B[0;36mhas_torch_function\u001B[0;34m(relevant_args)\u001B[0m\n\u001B[1;32m   1081\u001B[0m     \u001B[0mimplementations\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;32mFalse\u001B[0m \u001B[0motherwise\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1082\u001B[0m     \"\"\"\n\u001B[0;32m-> 1083\u001B[0;31m     return _is_torch_function_enabled() and any(\n\u001B[0m\u001B[1;32m   1084\u001B[0m         \u001B[0mtype\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0ma\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0mtorch\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mTensor\u001B[0m \u001B[0;32mand\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1085\u001B[0m         \u001B[0mgetattr\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0ma\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m'__torch_function__'\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0m_disabled_torch_function_impl\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/miniconda/envs/torch/lib/python3.8/site-packages/torch/overrides.py\u001B[0m in \u001B[0;36m<genexpr>\u001B[0;34m(.0)\u001B[0m\n\u001B[1;32m   1081\u001B[0m     \u001B[0mimplementations\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;32mFalse\u001B[0m \u001B[0motherwise\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1082\u001B[0m     \"\"\"\n\u001B[0;32m-> 1083\u001B[0;31m     return _is_torch_function_enabled() and any(\n\u001B[0m\u001B[1;32m   1084\u001B[0m         \u001B[0mtype\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0ma\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0mtorch\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mTensor\u001B[0m \u001B[0;32mand\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1085\u001B[0m         \u001B[0mgetattr\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0ma\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m'__torch_function__'\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0m_disabled_torch_function_impl\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/ml/labmlai/annotated_deep_learning_paper_implementations/labml_helpers/training_loop.py\u001B[0m in \u001B[0;36m__handler\u001B[0;34m(self, sig, frame)\u001B[0m\n\u001B[1;32m    162\u001B[0m             \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m__finish\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    163\u001B[0m             \u001B[0mlogger\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mlog\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m'Killing loop...'\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mText\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdanger\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 164\u001B[0;31m             \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mold_handler\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0msig\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mframe\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    165\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    166\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0m__str__\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "oBXXlP2b7XZO"
   },
   "source": [
    ""
   ],
   "execution_count": null,
   "outputs": []
  }
 ]
}